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• What is Audience Segmentation ? The 5 Main Types & Examples

  16 novembre 2023, par Erin — Analytics Tips

  The days of mass marketing with the same message for millions are long gone. Today, savvy marketers instead focus on delivering the most relevant message to the right person at the right time.

  They do this at scale by segmenting their audiences based on various data points. This isn’t an easy process because there are many types of audience segmentation. If you take the wrong approach, you risk delivering irrelevant messages to your audience — or breaking their trust with poor data management.

  In this article, we’ll break down the most common types of audience segmentation, share examples highlighting their usefulness and cover how you can segment campaigns without breaking data regulations.

  What is audience segmentation ?

  Audience segmentation is when you divide your audience into multiple smaller specific audiences based on various factors. The goal is to deliver a more targeted marketing message or to glean unique insights from analytics.

  It can be as broad as dividing a marketing campaign by location or as specific as separating audiences by their interests, hobbies and behaviour.

  

  Audience segmentation inherently makes a lot of sense. Consider this : an urban office worker and a rural farmer have vastly different needs. By targeting your marketing efforts towards agriculture workers in rural areas, you’re honing in on a group more likely to be interested in farm equipment. 

  Audience segmentation has existed since the beginning of marketing. Advertisers used to select magazines and placements based on who typically read them. They would run a golf club ad in a golf magazine, not in the national newspaper.

  How narrow you can make your audience segments by leveraging multiple data points has changed.

  Why audience segmentation matters

  In a survey by McKinsey, 71% of consumers said they expected personalisation, and 76% get frustrated when a vendor doesn’t deliver.

  

  These numbers reflect expectations from consumers who have actively engaged with a brand — created an account, signed up for an email list or purchased a product.

  They expect you to take that data and give them relevant product recommendations — like a shoe polishing kit if you bought nice leather loafers.

  If you don’t do any sort of audience segmentation, you’re likely to frustrate your customers with post-sale campaigns. If, for example, you just send the same follow-up email to all customers, you’d damage many relationships. Some might ask : “What ? Why would you think I need that ?” Then they’d promptly opt out of your email marketing campaigns.

  To avoid that, you need to segment your audience so you can deliver relevant content at all stages of the customer journey.

  5 key types of audience segmentation

  To help you deliver the right content to the right person or identify crucial insights in analytics, you can use five types of audience segmentation : demographic, behavioural, psychographic, technographic and transactional.

  

  Demographic segmentation 

  Demographic segmentation is when you segment a larger audience based on demographic data points like location, age or other factors.

  The most basic demographic segmentation factor is location, which is easy to leverage in marketing efforts. For example, geographic segmentation can use IP addresses and separate marketing efforts by country. 

  But more advanced demographic data points are becoming increasingly sensitive to handle. Especially in Europe, GDPR makes advanced demographics a more tentative subject. Using age, education level and employment to target marketing campaigns is possible. But you need to navigate this terrain thoughtfully and responsibly, ensuring meticulous adherence to privacy regulations.

  Potential data points :

  • Location
  • Age
  • Marital status
  • Income
  • Employment 
  • Education

  Example of effective demographic segmentation :

  A clothing brand targeting diverse locations needs to account for the varying weather conditions. In colder regions, showcasing winter collections or insulated clothing might resonate more with the audience. Conversely, in warmer climates, promoting lightweight or summer attire could be more effective. 

  Here are two ads run by North Face on Facebook and Instagram to different audiences to highlight different collections :

  

  Each collection is featured differently and uses a different approach with its copy and even the media. With social media ads, targeting people based on advanced demographics is simple enough — you can just single out the factors when making your campaign. But if you don’t want to rely on these data-mining companies, that doesn’t mean you have no options for segmentation.

  Consider allowing people to self-select their interests or preferences by incorporating a short survey within your email sign-up form. This simple addition can enhance engagement, decrease bounce rates, and ultimately improve conversion rates, offering valuable insights into audience preferences.

  This is a great way to segment ethically and without the need of data-mining companies.

  Behavioural segmentation

  Behavioural segmentation segments audiences based on their interaction with your website or app.

  You use various data points to segment your target audience based on their actions.

  Potential data points :

  • Page visits
  • Referral source
  • Clicks
  • Downloads
  • Video plays
  • Goal completion (e.g., signing up for a newsletter or purchasing a product)

  Example of using behavioural segmentation to improve campaign efficiency :

  One effective method involves using a web analytics tool such as Matomo to uncover patterns. By segmenting actions like specific clicks and downloads, pinpoint valuable trends—identifying actions that significantly enhance visitor conversions. 

  

  For instance, if a case study video substantially boosts conversion rates, elevate its prominence to capitalise on this success.

  Then, you can set up a conditional CTA within the video player. Make it pop up after the user has watched the entire video. Use a specific form and sign them up to a specific segment for each case study. This way, you know the prospect’s ideal use case without surveying them.

  This is an example of behavioural segmentation that doesn’t rely on third-party cookies.

  Psychographic segmentation

  Psychographic segmentation is when you segment audiences based on your interpretation of their personality or preferences.

  Potential data points :

  • Social media patterns
  • Follows
  • Hobbies
  • Interests

  Example of effective psychographic segmentation :

  Here, Adidas segments its audience based on whether they like cycling or rugby. It makes no sense to show a rugby ad to someone who’s into cycling and vice versa. But to rugby athletes, the ad is very relevant.

  

  If you want to avoid social platforms, you can use surveys about hobbies and interests to segment your target audience in an ethical way.

  Technographic segmentation

  Technographic segmentation is when you single out specific parts of your audience based on which hardware or software they use.

  Potential data points :

  • Type of device used
  • Device model or brand
  • Browser used

  Example of segmenting by device type to improve user experience :

  Upon noticing a considerable influx of tablet users accessing their platform, a leading news outlet decided to optimise their tablet browsing experience. They overhauled the website interface, focusing on smoother navigation and better readability for tablet users. These changes offered tablet users a seamless and enjoyable reading experience tailored precisely to their device.

  Transactional segmentation

  Transactional segmentation is when you use your customers’ purchase history to better target your marketing message to their needs.

  When consumers prefer personalisation, they typically mean based on their actual transactions, not their social media profiles.

  Potential data points :

  • Average order value
  • Product categories purchased within X months
  • X days since the last purchase of a consumable product

  Example of effective transactional segmentation :

  A pet supply store identifies a segment of customers consistently purchasing cat food but not other pet products. They create targeted email campaigns offering discounts or loyalty rewards specifically for cat-related items to encourage repeat purchases within this segment.

  If you want to improve customer loyalty and increase revenue, the last thing you should do is send generic marketing emails. Relevant product recommendations or coupons are the best way to use transactional segmentation.

  B2B-specific : Firmographic segmentation

  Beyond the five main segmentation types, B2B marketers often use “firmographic” factors when segmenting their campaigns. It’s a way to segment campaigns that go beyond the considerations of the individual.

  Potential data points :

  • Company size
  • Number of employees
  • Company industry
  • Geographic location (office)

  Example of effective firmographic segmentation :

  Companies of different sizes won’t need the same solution — so segmenting leads by company size is one of the most common and effective examples of B2B audience segmentation.

  The difference here is that B2B campaigns are often segmented through manual research. With an account-based marketing approach, you start by researching your potential customers. You then separate the target audience into smaller segments (or even a one-to-one campaign).

  Start segmenting and analysing your audience more deeply with Matomo

  Segmentation is a great place to start if you want to level up your marketing efforts. Modern consumers expect to get relevant content, and you must give it to them.

  But doing so in a privacy-sensitive way is not always easy. You need the right approach to segment your customer base without alienating them or breaking regulations.

  That’s where Matomo comes in. Matomo champions privacy compliance while offering comprehensive insights and segmentation capabilities. With robust privacy controls and cookieless configuration, it ensures GDPR and other regulations are met, empowering data-driven decisions without compromising user privacy.

  Take advantage of our 21-day free trial to get insights that can help you improve your marketing strategy and better reach your target audience. No credit card required.

• Custom Segmentation Guide : How it Works & Segments to Test

  13 novembre 2023, par Erin — Analytics Tips, Uncategorized

  Struggling to get the insights you’re looking for with premade reports and audience segments in your analytics ?

  Custom segmentation can help you better understand your customers, app users or website visitors, but only if you know what you’re doing.

  You can derive false insights with the wrong segments, leading your marketing campaigns or product development in the wrong direction.

  In this article, we’ll break down what custom segmentation is, useful custom segments to consider, how new privacy laws affect segmentation options and how to create these segments in an analytics platform.

  What is custom segmentation ?

  Custom segmentation is when you divide your audience (customers, users, website visitors) into bespoke segments of your own design, not premade segments designed by the analytics or marketing platform provider.

  To do this, you single out “custom segment input” — data points you will use to pinpoint certain users. For example, it could be everyone who has visited a certain page on your site.

  

  Segmentation isn’t just useful for targeting marketing campaigns and also for analysing your customer data. Creating segments is a great way to dive deeper into your data beyond surface-level insights.

  You can explore how various factors impact engagement, conversion rates, and customer lifetime value. These insights can help guide your higher-level strategy, not just campaigns.

  How custom segments can help your business

  As the global business world clamours to become more “data-driven,” even smaller companies collect all sorts of data on visitors, users, and customers.

  However, inexperienced organisations often become “data hoarders” without meaningful insights. They have in-house servers full of data or gigabytes stored by Google Analytics and other third-party providers.

  

  One way to leverage this data is with standard customer segmentation models. This can help you get insights into your most valuable customer groups and other standard segments.

  Custom segments, in turn, can help you dive deeper. They help you unlock insights into the “why” of certain behaviours. They can help you segment customers and your audience to figure out :

  • Why and how someone became a loyal customer
  • How high-order-value customers interact with your site before purchases
  • Which behaviours indicate audience members are likely to convert
  • Which traffic sources drive the most valuable customers

  This specific insight’s power led Gartner to predict that 70% of companies will shift focus from “big data” to “small and wide” by 2025. The lateral detail is what helps inform your marketing strategy. 

  You don’t need the same volume of data if you’re analysing and segmenting it effectively.

  Custom segment inputs : 6 data points you can use to create valuable custom segments 

  To help you get started, here are six useful data points you can use as a basis to create segments — AKA customer segment inputs :

  

  Visits to certain pages

  A basic data point that’s great for custom segments is visits to certain pages. Create segments for popular middle-of-funnel pages and compare their engagement and conversion rates. 

  For example, if a user visits a case study page, you can compare their likelihood to convert vs. other visitors.

  This is a type of behavioural segmentation, but it is the easiest custom segment to set up in terms of analysis and marketing efforts.

  Visitors who perform certain actions

  The other important type of behavioural segment is visitors or users who take certain actions. Think of things like downloading a file, clicking a link, playing a video or scrolling a certain amount.

  For instance, you can create a segment of all visitors who have downloaded a white paper. This can help you explore, for example, what drives someone to download a white paper. You can look at the typical user journey and make it easier for them to access the white paper — especially if your sales reps indicate many inbound leads mention it as a key driver of their interest.

  User devices

  Device-based segmentation lets you compare engagement and conversion rates on mobile, desktop and tablets. You can also get insights into their usage patterns and potential issues with certain mobile elements.

  

  This is one aspect of technographic segmentation, where you segment based on users’ hardware or software. You can also create segments based on browser software or even specific versions.

  Loyal or high-value customers

  The best way to get more loyal or high-value customers is to explore their journey in more detail. These types of segments can help you better understand your ideal customers and how they act on your site.

  You can then use this insight to alter your campaigns or how you communicate with your target audience.

  For example, you might notice that high-value customers tend to come from a certain source. You can then focus your marketing efforts on this source to reach more of your ideal customers.

  Visitor or customer source

  You need to track the results if you’re investing in marketing (like an influencer campaign or a sponsored post) outside platforms with their own analytics.

  

  Before you can create a reliable segment, you need to make sure that you use campaign tracking parameters to reliably track the source. You can use our free campaign tracking URL builder for that.

  Demographic segments — location (country, state) and more

  Web analytics tools, such as Matomo, use visitors’ IP addresses to pinpoint their location more accurately by cross-referencing with a database of known and estimated IP locations. In addition, these tools can detect a visitor’s location through the language settings in their browser. 

  This can help create segments based on location or language. By exploring these trends, you can identify patterns in behaviour, tailor your content to specific audiences, and adapt your overall strategy to better meet the preferences and needs of your diverse visitor base.

  How new privacy laws affect segmentation options

  Over the past few years, new legislation regarding privacy and customer data has been passed globally. The most notable privacy laws are the GDPR in the EU, the CCPA in California and the VCDPA in Virginia.

  

  For most companies, it can save a lot of work and future headaches to choose a GDPR-compliant web analytics solution not only streamlines operations, saving considerable effort and preventing future headaches, but also ensures peace of mind by guaranteeing the collection of compliant and accurate data. This approach allows companies to maintain compliance with privacy regulations while remaining firmly committed to a data-driven strategy.

  Create your very own custom segments in Matomo (while ensuring compliance and data accuracy)

  Crafting precise marketing messages and optimising ROI is crucial, but it becomes challenging without the right tools, especially when it comes to maintaining accurate data.

  That’s where Matomo comes in. Our privacy-friendly web analytics platform is GDPR-compliant and ensures accurate data, empowering you to effortlessly create and analyse precise custom segments.

  If you want to improve your marketing campaigns while remaining GDPR-compliant, start your 21-day free trial of Matomo. No credit card required.

• Save video using opencv with H264 codec

  31 octobre 2023, par ldiaz997

  This is beyond me and I don't know what I'm doing wrong. I have read that in order to have my video in h265 codec, I need to build opencv from source. Well, I did that, and I also did it for ffmpeg Docker ffmpeg Compiler. But I'm trying to run my application using docker, and I still can't get over the error :

  


  [ERROR:0@93.327] global cap_ffmpeg_impl.hpp:3018 open Could not find encoder for codec_id=27, error: Encoder not found
[ERROR:0@93.327] global cap_ffmpeg_impl.hpp:3093 open VIDEOIO/FFMPEG: Failed to initialize VideoWriter


  


  Dockerfile :

  


  FROM python:3.10.12-slim-buster

RUN apt-get update

# Set the working directory in the container
WORKDIR /app

# Copy the application code into the container
COPY . .

# Set ffmpeg and ffprobe binary files
RUN mv ffmpeg /usr/local/bin
RUN mv ffprobe /usr/local/bin

# Build opencv from source, to be able to use h264 codec.
RUN apt-get install -y cmake \
    gcc \
    g++ \
    python3-numpy \
    libavcodec-dev \
    libavformat-dev \
    libswscale-dev \
    libgstreamer-plugins-base1.0-dev \
    libgstreamer1.0-dev \
    libpng-dev \
    libjpeg-dev \
    libopenexr-dev \
    libtiff-dev \
    libwebp-dev \
    git

RUN git clone --depth 1 --branch 4.8.0 https://github.com/opencv/opencv.git && \
    git clone --depth 1 --branch 4.8.0 https://github.com/opencv/opencv_contrib.git && \
    cd opencv && \
    mkdir build && \
    cd build && \
    cmake -D OPENCV_EXTRA_MODULES_PATH=/app/opencv_contrib/modules ../ && \
    make -j"$(nproc)" && \
    make install

# Remove opencv github project
RUN rm -r opencv

# Remove opencv_contrib github project
RUN rm -r opencv_contrib

# Prevents Python from writing pyc files to disc
ENV PYTHONDONTWRITEBYTECODE 1

# Prevents Python from buffering stdout and stderr
ENV PYTHONUNBUFFERED 1

# Install python dependencies
RUN pip install --upgrade pip
RUN pip install --no-cache-dir -r requirements.txt

# Install netcat to know when rabbitmq is running
RUN apt-get install -y netcat

# Set execute permissions
RUN chmod +x entrypoint.sh
RUN chmod +x web_start.sh

ENTRYPOINT ["./entrypoint.sh"]


  


  I ran the command ./ffmpeg -i 57b3e3a7-ad22-469d-a7ff-cf76ba780664 -vcodec libx264 -acodec aac output.mp4 to test ffmpeg and this was the result.

  


  ffmpeg version N-112515-gba6a5e7a3d Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 5.4.0 (Ubuntu 5.4.0-6ubuntu1~16.04.12) 20160609
  configuration: --prefix=/root/ffmpeg_build --pkg-config-flags=--static --extra-libs=-static --extra-cflags=--static --extra-cflags=-I/root/ffmpeg_build/include --extra-ldflags=-L/root/ffmpeg_build/lib --extra-libs='-lpthread -lm' --bindir=/root/bin --enable-gpl --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libtheora --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-nonfree
  libavutil      58. 27.100 / 58. 27.100
  libavcodec     60. 30.102 / 60. 30.102
  libavformat    60. 15.101 / 60. 15.101
  libavdevice    60.  2.101 / 60.  2.101
  libavfilter     9. 11.100 /  9. 11.100
  libswscale      7.  4.100 /  7.  4.100
  libswresample   4. 11.100 /  4. 11.100
  libpostproc    57.  2.100 / 57.  2.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '57b3e3a7-ad22-469d-a7ff-cf76ba780664':
  Metadata:
    major_brand     : qt  
    minor_version   : 0
    compatible_brands: qt  
    creation_time   : 2023-10-30T15:34:32.000000Z
    com.apple.quicktime.make: Apple
    com.apple.quicktime.model: iPhone 13 Pro Max
    com.apple.quicktime.software: 16.6
    com.apple.quicktime.creationdate: 2023-10-30T11:34:32-0400
  Duration: 00:00:03.60, start: 0.000000, bitrate: 16264 kb/s
  Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1920x1080, 16120 kb/s, 29.99 fps, 29.97 tbr, 600 tbn (default)
    Metadata:
      creation_time   : 2023-10-30T15:34:32.000000Z
      handler_name    : Core Media Video
      vendor_id       : [0][0][0][0]
      encoder         : H.264
    Side data:
      displaymatrix: rotation of -90.00 degrees
  Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, mono, fltp, 89 kb/s (default)
    Metadata:
      creation_time   : 2023-10-30T15:34:32.000000Z
      handler_name    : Core Media Audio
      vendor_id       : [0][0][0][0]
  Stream #0:2[0x3](und): Data: none (mebx / 0x7862656D), 0 kb/s (default)
    Metadata:
      creation_time   : 2023-10-30T15:34:32.000000Z
      handler_name    : Core Media Metadata
  Stream #0:3[0x4](und): Data: none (mebx / 0x7862656D), 0 kb/s (default)
    Metadata:
      creation_time   : 2023-10-30T15:34:32.000000Z
      handler_name    : Core Media Metadata
  Stream #0:4[0x5](und): Data: none (mebx / 0x7862656D), 34 kb/s (default)
    Metadata:
      creation_time   : 2023-10-30T15:34:32.000000Z
      handler_name    : Core Media Metadata
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
  Stream #0:1 -> #0:1 (aac (native) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 0x5ae4c00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 AVX2 LZCNT BMI2
[libx264 @ 0x5ae4c00] profile High, level 4.0
[libx264 @ 0x5ae4c00] 264 - core 148 r2643 5c65704 - H.264/MPEG-4 AVC codec - Copyleft 2003-2015 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=30 lookahead_threads=5 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'output.mp4':
  Metadata:
    major_brand     : qt  
    minor_version   : 0
    compatible_brands: qt  
    com.apple.quicktime.creationdate: 2023-10-30T11:34:32-0400
    com.apple.quicktime.make: Apple
    com.apple.quicktime.model: iPhone 13 Pro Max
    com.apple.quicktime.software: 16.6
    encoder         : Lavf60.15.101
  Stream #0:0(und): Video: h264 (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1080x1920, q=2-31, 29.97 fps, 30k tbn (default)
    Metadata:
      creation_time   : 2023-10-30T15:34:32.000000Z
      handler_name    : Core Media Video
      vendor_id       : [0][0][0][0]
      encoder         : Lavc60.30.102 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
      displaymatrix: rotation of -0.00 degrees
  Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, mono, fltp, 69 kb/s (default)
    Metadata:
      creation_time   : 2023-10-30T15:34:32.000000Z
      handler_name    : Core Media Audio
      vendor_id       : [0][0][0][0]
      encoder         : Lavc60.30.102 aac
[out#0/mp4 @ 0x5ae3440] video:2773kB audio:31kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.157082%
frame=  108 fps= 74 q=-1.0 Lsize=    2809kB time=00:00:03.59 bitrate=6393.3kbits/s speed=2.47x    
[libx264 @ 0x5ae4c00] frame I:4     Avg QP:22.27  size: 48408
[libx264 @ 0x5ae4c00] frame P:104   Avg QP:24.58  size: 25440
[libx264 @ 0x5ae4c00] mb I  I16..4: 10.3% 82.9%  6.8%
[libx264 @ 0x5ae4c00] mb P  I16..4:  4.6% 18.1%  0.8%  P16..4: 40.3%  6.9%  4.1%  0.0%  0.0%    skip:25.3%
[libx264 @ 0x5ae4c00] 8x8 transform intra:78.0% inter:85.0%
[libx264 @ 0x5ae4c00] coded y,uvDC,uvAC intra: 44.9% 29.1% 0.1% inter: 22.5% 23.3% 0.0%
[libx264 @ 0x5ae4c00] i16 v,h,dc,p: 17% 49% 14% 19%
[libx264 @ 0x5ae4c00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19% 25% 37%  3%  3%  5%  3%  2%  4%
[libx264 @ 0x5ae4c00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 29% 30% 17%  3%  4%  8%  3%  2%  3%
[libx264 @ 0x5ae4c00] i8c dc,h,v,p: 67% 20% 12%  0%
[libx264 @ 0x5ae4c00] Weighted P-Frames: Y:1.9% UV:0.0%
[libx264 @ 0x5ae4c00] ref P L0: 61.8% 10.4% 18.3%  9.4%  0.2%
[libx264 @ 0x5ae4c00] kb/s:6303.40
[aac @ 0x68c9880] Qavg: 119.986


  


  The resulting video had an h264 codec. In my opinion, the problem is in opencv. Basically this is what I do in my python code :

  


  cap = cv2.VideoCapture(video)
shoot_frames = []
while True:
    ret, img = cap.read()
    if not ret:
       break
    if some_condition:
       shoot_frames.append(img)
    if len(shoot_frames) > 41:
       out1 = cv2.VideoWriter(upload_path(name , dir), cv2.VideoWriter_fourcc(*'avc1'), int(fps), (int(width), int(height)), True)
       for shoot_frame in shoot_frames:
           out1.write(shoot_frame)
       out1.release()
       shoot_frames = []


  


  Output from print(cv2.getBuildInformation()) :

  


  General configuration for OpenCV 4.8.1 =====================================
  Version control:               4.8.1-dirty

  Platform:
    Timestamp:                   2023-09-27T14:20:56Z
    Host:                        Linux 5.15.0-1046-azure x86_64
    CMake:                       3.27.5
    CMake generator:             Unix Makefiles
    CMake build tool:            /bin/gmake
    Configuration:               Release

  CPU/HW features:
    Baseline:                    SSE SSE2 SSE3
      requested:                 SSE3
    Dispatched code generation:  SSE4_1 SSE4_2 FP16 AVX AVX2 AVX512_SKX
      requested:                 SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
      SSE4_1 (16 files):         + SSSE3 SSE4_1
      SSE4_2 (1 files):          + SSSE3 SSE4_1 POPCNT SSE4_2
      FP16 (0 files):            + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
      AVX (7 files):             + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
      AVX2 (35 files):           + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2
      AVX512_SKX (5 files):      + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2 AVX_512F AVX512_COMMON AVX512_SKX

  C/C++:
    Built as dynamic libs?:      NO
    C++ standard:                11
    C++ Compiler:                /opt/rh/devtoolset-10/root/usr/bin/c++  (ver 10.2.1)
    C++ flags (Release):         -Wl,-strip-all   -fsigned-char -W -Wall -Wreturn-type -Wnon-virtual-dtor -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG
    C++ flags (Debug):           -Wl,-strip-all   -fsigned-char -W -Wall -Wreturn-type -Wnon-virtual-dtor -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG
    C Compiler:                  /opt/rh/devtoolset-10/root/usr/bin/cc
    C flags (Release):           -Wl,-strip-all   -fsigned-char -W -Wall -Wreturn-type -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -O3 -DNDEBUG  -DNDEBUG
    C flags (Debug):             -Wl,-strip-all   -fsigned-char -W -Wall -Wreturn-type -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -g  -O0 -DDEBUG -D_DEBUG
    Linker flags (Release):      -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a -L/ffmpeg_build/lib  -Wl,--gc-sections -Wl,--as-needed -Wl,--no-undefined  
    Linker flags (Debug):        -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a -L/ffmpeg_build/lib  -Wl,--gc-sections -Wl,--as-needed -Wl,--no-undefined  
    ccache:                      YES
    Precompiled headers:         NO
    Extra dependencies:          /lib64/libopenblas.so Qt5::Core Qt5::Gui Qt5::Widgets Qt5::Test Qt5::Concurrent /usr/local/lib/libpng.so /lib64/libz.so dl m pthread rt
    3rdparty dependencies:       libprotobuf ade ittnotify libjpeg-turbo libwebp libtiff libopenjp2 IlmImf quirc ippiw ippicv

  OpenCV modules:
    To be built:                 calib3d core dnn features2d flann gapi highgui imgcodecs imgproc ml objdetect photo python3 stitching video videoio
    Disabled:                    world
    Disabled by dependency:      -
    Unavailable:                 java python2 ts
    Applications:                -
    Documentation:               NO
    Non-free algorithms:         NO

  GUI:                           QT5
    QT:                          YES (ver 5.15.0 )
      QT OpenGL support:         NO
    GTK+:                        NO
    VTK support:                 NO

  Media I/O: 
    ZLib:                        /lib64/libz.so (ver 1.2.7)
    JPEG:                        libjpeg-turbo (ver 2.1.3-62)
    WEBP:                        build (ver encoder: 0x020f)
    PNG:                         /usr/local/lib/libpng.so (ver 1.6.40)
    TIFF:                        build (ver 42 - 4.2.0)
    JPEG 2000:                   build (ver 2.5.0)
    OpenEXR:                     build (ver 2.3.0)
    HDR:                         YES
    SUNRASTER:                   YES
    PXM:                         YES
    PFM:                         YES

  Video I/O:
    DC1394:                      NO
    FFMPEG:                      YES
      avcodec:                   YES (59.37.100)
      avformat:                  YES (59.27.100)
      avutil:                    YES (57.28.100)
      swscale:                   YES (6.7.100)
      avresample:                NO
    GStreamer:                   NO
    v4l/v4l2:                    YES (linux/videodev2.h)

  Parallel framework:            pthreads

  Trace:                         YES (with Intel ITT)

  Other third-party libraries:
    Intel IPP:                   2021.8 [2021.8.0]
           at:                   /io/_skbuild/linux-x86_64-3.7/cmake-build/3rdparty/ippicv/ippicv_lnx/icv
    Intel IPP IW:                sources (2021.8.0)
              at:                /io/_skbuild/linux-x86_64-3.7/cmake-build/3rdparty/ippicv/ippicv_lnx/iw
    VA:                          NO
    Lapack:                      YES (/lib64/libopenblas.so)
    Eigen:                       NO
    Custom HAL:                  NO
    Protobuf:                    build (3.19.1)
    Flatbuffers:                 builtin/3rdparty (23.5.9)

  OpenCL:                        YES (no extra features)
    Include path:                /io/opencv/3rdparty/include/opencl/1.2
    Link libraries:              Dynamic load

  Python 3:
    Interpreter:                 /opt/python/cp37-cp37m/bin/python3.7 (ver 3.7.17)
    Libraries:                   libpython3.7m.a (ver 3.7.17)
    numpy:                       /home/ci/.local/lib/python3.7/site-packages/numpy/core/include (ver 1.17.0)
    install path:                python/cv2/python-3

  Python (for build):            /opt/python/cp37-cp37m/bin/python3.7

  Java:                          
    ant:                         NO
    Java:                        NO
    JNI:                         NO
    Java wrappers:               NO
    Java tests:                  NO

  Install to:                    /io/_skbuild/linux-x86_64-3.7/cmake-install
-----------------------------------------------------------------




  


  Update

  


  I made my docker image more simpler, and therefore my question. Install ffmpeg from the repository :

  


  FROM python:3.10.12-slim-buster

RUN apt-get update

# Set the working directory in the container
WORKDIR /app

# Install ffmpeg for opencv
RUN apt-get install -y ffmpeg

# Copy the application code into the container
COPY . .

# Build opencv from source, to be able to use h264 codec.
RUN apt-get install -y cmake \
    gcc \
    g++ \
    python3-numpy \
    libavcodec-dev \
    libavformat-dev \
    libswscale-dev \
    libgstreamer-plugins-base1.0-dev \
    libgstreamer1.0-dev \
    libpng-dev \
    libjpeg-dev \
    libopenexr-dev \
    libtiff-dev \
    libwebp-dev \
    git

RUN git clone --depth 1 --branch 4.8.0 https://github.com/opencv/opencv.git && \
    git clone --depth 1 --branch 4.8.0 https://github.com/opencv/opencv_contrib.git && \
    cd opencv && \
    mkdir build && \
    cd build && \
    cmake -D CMAKE_BUILD_TYPE=Release -D OPENCV_EXTRA_MODULES_PATH=/app/opencv_contrib/modules -D OPENCV_ENABLE_NONFREE=ON ../ && \
    make -j"$(nproc)" && \
    make install

# Remove opencv github project
RUN rm -r opencv

# Remove opencv_contrib github project
RUN rm -r opencv_contrib

# Prevents Python from writing pyc files to disc
ENV PYTHONDONTWRITEBYTECODE 1

# Prevents Python from buffering stdout and stderr
ENV PYTHONUNBUFFERED 1

# Install python dependencies
RUN pip install --upgrade pip
RUN pip install --no-cache-dir -r requirements.txt

# Install netcat to know when rabbitmq is running
RUN apt-get install -y netcat

# Set execute permissions
RUN chmod +x entrypoint.sh
RUN chmod +x web_start.sh

ENTRYPOINT ["./entrypoint.sh"]


  


  Run the following commands inside the docker container :

  


  $ ffmpeg -version

ffmpeg version 4.1.11-0+deb10u1 Copyright (c) 2000-2023 the FFmpeg developers
built with gcc 8 (Debian 8.3.0-6)
configuration: --prefix=/usr --extra-version=0+deb10u1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
libavutil      56. 22.100 / 56. 22.100
libavcodec     58. 35.100 / 58. 35.100
libavformat    58. 20.100 / 58. 20.100
libavdevice    58.  5.100 / 58.  5.100
libavfilter     7. 40.101 /  7. 40.101
libavresample   4.  0.  0 /  4.  0.  0
libswscale      5.  3.100 /  5.  3.100
libswresample   3.  3.100 /  3.  3.100
libpostproc    55.  3.100 / 55.  3.100


  


  $ ffmpeg -i cf91f302-c357-49ba-b59c-bcfb8b7f4866 -vcodec libx264 -f mp4 output.mp4

ffmpeg version 4.1.11-0+deb10u1 Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 8 (Debian 8.3.0-6)
  configuration: --prefix=/usr --extra-version=0+deb10u1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 22.100 / 56. 22.100
  libavcodec     58. 35.100 / 58. 35.100
  libavformat    58. 20.100 / 58. 20.100
  libavdevice    58.  5.100 / 58.  5.100
  libavfilter     7. 40.101 /  7. 40.101
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  3.100 /  5.  3.100
  libswresample   3.  3.100 /  3.  3.100
  libpostproc    55.  3.100 / 55.  3.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'cf91f302-c357-49ba-b59c-bcfb8b7f4866':
  Metadata:
    major_brand     : qt  
    minor_version   : 0
    compatible_brands: qt  
    creation_time   : 2023-10-31T10:38:42.000000Z
    com.apple.quicktime.make: Apple
    com.apple.quicktime.model: iPhone 13 Pro Max
    com.apple.quicktime.software: 16.6
    com.apple.quicktime.creationdate: 2023-10-31T06:38:42-0400
  Duration: 00:00:04.23, start: 0.000000, bitrate: 15915 kb/s
    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1920x1080, 15767 kb/s, 30 fps, 30 tbr, 600 tbn, 1200 tbc (default)
    Metadata:
      rotate          : 90
      creation_time   : 2023-10-31T10:38:42.000000Z
      handler_name    : Core Media Video
      encoder         : H.264
    Side data:
      displaymatrix: rotation of -90.00 degrees
    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, mono, fltp, 89 kb/s (default)
    Metadata:
      creation_time   : 2023-10-31T10:38:42.000000Z
      handler_name    : Core Media Audio
    Stream #0:2(und): Data: none (mebx / 0x7862656D), 0 kb/s (default)
    Metadata:
      creation_time   : 2023-10-31T10:38:42.000000Z
      handler_name    : Core Media Metadata
    Stream #0:3(und): Data: none (mebx / 0x7862656D), 0 kb/s (default)
    Metadata:
      creation_time   : 2023-10-31T10:38:42.000000Z
      handler_name    : Core Media Metadata
    Stream #0:4(und): Data: none (mebx / 0x7862656D), 34 kb/s (default)
    Metadata:
      creation_time   : 2023-10-31T10:38:42.000000Z
      handler_name    : Core Media Metadata
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
  Stream #0:1 -> #0:1 (aac (native) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 0x55db965ee980] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x55db965ee980] profile High, level 4.0
[libx264 @ 0x55db965ee980] 264 - core 155 r2917 0a84d98 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'output.mp4':
  Metadata:
    major_brand     : qt  
    minor_version   : 0
    compatible_brands: qt  
    com.apple.quicktime.creationdate: 2023-10-31T06:38:42-0400
    com.apple.quicktime.make: Apple
    com.apple.quicktime.model: iPhone 13 Pro Max
    com.apple.quicktime.software: 16.6
    encoder         : Lavf58.20.100
    Stream #0:0(und): Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 1080x1920, q=-1--1, 30 fps, 15360 tbn, 30 tbc (default)
    Metadata:
      encoder         : Lavc58.35.100 libx264
      creation_time   : 2023-10-31T10:38:42.000000Z
      handler_name    : Core Media Video
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
      displaymatrix: rotation of -0.00 degrees
    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, mono, fltp, 69 kb/s (default)
    Metadata:
      creation_time   : 2023-10-31T10:38:42.000000Z
      handler_name    : Core Media Audio
      encoder         : Lavc58.35.100 aac
frame=  127 fps= 27 q=-1.0 Lsize=    2005kB time=00:00:04.24 bitrate=3866.2kbits/s speed=0.909x    
video:1964kB audio:36kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.282549%
[libx264 @ 0x55db965ee980] frame I:1     Avg QP:21.43  size: 36791
[libx264 @ 0x55db965ee980] frame P:59    Avg QP:23.61  size: 22380
[libx264 @ 0x55db965ee980] frame B:67    Avg QP:24.20  size:  9743
[libx264 @ 0x55db965ee980] consecutive B-frames: 20.5% 22.0% 16.5% 40.9%
[libx264 @ 0x55db965ee980] mb I  I16..4: 29.4% 58.6% 11.9%
[libx264 @ 0x55db965ee980] mb P  I16..4: 15.0% 21.8%  1.3%  P16..4: 26.1%  7.5%  3.1%  0.0%  0.0%    skip:25.2%
[libx264 @ 0x55db965ee980] mb B  I16..4:  1.9%  1.7%  0.1%  B16..8: 36.3%  3.6%  0.5%  direct: 3.9%  skip:52.1%  L0:42.9% L1:52.1% BI: 5.0%
[libx264 @ 0x55db965ee980] 8x8 transform intra:56.2% inter:86.6%
[libx264 @ 0x55db965ee980] coded y,uvDC,uvAC intra: 19.5% 27.3% 2.1% inter: 11.7% 18.9% 0.1%
[libx264 @ 0x55db965ee980] i16 v,h,dc,p: 25% 54%  8% 12%
[libx264 @ 0x55db965ee980] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 25% 44%  1%  2%  2%  2%  1%  1%
[libx264 @ 0x55db965ee980] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 16% 45% 13%  2%  7%  6%  6%  3%  3%
[libx264 @ 0x55db965ee980] i8c dc,h,v,p: 62% 27% 10%  1%
[libx264 @ 0x55db965ee980] Weighted P-Frames: Y:3.4% UV:0.0%
[libx264 @ 0x55db965ee980] ref P L0: 65.2% 18.0% 12.2%  4.6%  0.1%
[libx264 @ 0x55db965ee980] ref B L0: 89.1%  9.3%  1.6%
[libx264 @ 0x55db965ee980] ref B L1: 97.2%  2.8%
[libx264 @ 0x55db965ee980] kb/s:3798.37
[aac @ 0x55db965edf00] Qavg: 125.454


  


  The errors persist.

  


  >>> import cv2
>>> out = cv2.VideoWriter("./out.mp4", cv2.VideoWriter_fourcc(*'avc1'), 30, (800, 600), True)
[ERROR:0@91.872] global cap_ffmpeg_impl.hpp:3018 open Could not find encoder for codec_id=27, error: Encoder not found
[ERROR:0@91.872] global cap_ffmpeg_impl.hpp:3093 open VIDEOIO/FFMPEG: Failed to initialize VideoWriter


  


  Could someone please tell me what I'm doing wrong ?